Question 1

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

Options :

A : Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

B : Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

C : Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

D : Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

Question 2

Analysts are using Cloud Data Studio for analyzing data sets. They would like to improve the performance of the time required to update tables and charts when working with the data. What would you recommend they try to improve performance?

Options :

A : Use an imported data source

B : Use a blended data source

C : Use a live data source

D : Use an extracted data source

Answer: D

Question 3

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

Options :

A : 500 TB

B : 1 GB

C : 1 TB

D : 500 GB

Answer: C

Question 4

Which of these are examples of a value in a sparse vector? (Select 2 answers.)

Options :

A : [0, 5, 0, 0, 0, 0]

B : [0, 0, 0, 1, 0, 0, 1]

C : [0, 1]

D : [1, 0, 0, 0, 0, 0, 0]

Answer: C,D

Question 5

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Options :

A : Create an authorized view in BigQuery to restrict access to tables with sensitive data.

B : Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information

C : Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information

D : Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: D

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